Machine Learning Skills you will learn

  • Supervised and Unsupervised Learning
  • Time Series Modeling
  • Linear and Logistic Regression
  • Kernel SVM
  • K-Means Clustering
  • Naive Bayes Classification
  • Decision Tree Construction
  • Random Forest Classifiers
  • Boosting and Bagging Techniques
  • Deep Learning Fundamentals

Who should learn this free Machine Learning course?

  • Analytics Managers
  • Business Analysts
  • Information Architects
  • Developers

What you will learn in this free Machine Learning course?

  • Machine Learning using Python

    • Lesson 01: Course Introduction

      • 1.01 Course Introduction
        02:39
      • 1.02 What You Will Learn
        01:55

    • Lesson 02: Introduction to Machine Learning

      • 2.01 Introduction
        00:51
      • 2.02 What Is Machine Learning?
        02:49
      • 2.03 Types of Machine Learning
        02:54
      • 2.04 Machine Learning Pipeline and MLOP's
        03:35
      • 2.05 Introduction to Python Packages Used in Machine Learning
        03:29
      • 2.06 Recap
        00:52

    • Lesson 03: Supervised Learning

      • 3.01 Introduction
        00:41
      • 3.02 Supervised Learning
        02:34
      • 3.03 Applications of Supervised Learning
        03:11
      • 3.04 Preparing and Shaping Data
        06:50
      • 3.05 What is overfitting and underfitting?
        02:23
      • 3.06 Detecting and Preventing Overfitting and Underfitting
        07:36
      • 3.07 Regularization
        02:38
      • 3.08 Recap
        00:41

    • Lesson 04: Regression and Applications

      • 4.01 Introduction
        01:14
      • 4.02 What is Regression?
        01:34
      • 4.03 Regression Types: Introduction
        02:45
      • 4.04 Linear Regression
        02:48
      • 4.05 Working with Linear Regression
        35:14
      • 4.06 Critical Assumptions for Linear Regression
        01:31
      • 4.07 Logistic Regression
        02:33
      • 4.08 Data Exploration Using SMOTE
        12:56
      • 4.09 Over Sampling Using SMOTE
        01:48
      • 4.10 Polynomial Regression
        02:41
      • 4.11 Data Preparation Model Building and Performance Evaluation Part A
        04:53
      • 4.12 Ridge Regression
        01:57
      • 4.13 Data Preparation Model Building: Part B
        06:25
      • 4.14 LASSO Regression
        02:30
      • 4.15 Data Preparation Model Building: Part C
        06:13
      • 4.16 Recap
        00:55
      • 4.17 Spotlight
        02:40

    • Lesson 05: Classification and Applications

      • 5.01 Introduction
        01:03
      • 5.02 What are Classification Algorithms?
        02:09
      • 5.03 Types of Classification
        03:29
      • 5.04 Types and selection of performance parameters
        04:58
      • 5.05 Naive Bayes
        02:56
      • 5.06 Applying Naive Bayes Classifier
        03:27
      • 5.07 Stochastic Gradient Descent
        03:25
      • 5.08 Applying Stochastic Gradient Descent
        05:02
      • 5.09 K Nearest Neighbors
        02:41
      • 5.10 Applying K Nearest Neighbors
        05:28
      • 5.11 Decision Tree
        02:42
      • 5.12 Applying Decision Tree
        04:27
      • 5.13 Random Forest
        01:59
      • 5.14 Applying Random Forest
        03:22
      • 5.15 Boruta Explained
        01:15
      • 5.16 Automatic Feature Selection with Boruta
        06:43
      • 5.17 Support Vector Machine
        02:27
      • 5.18 Applying Support Vector Machine
        07:07
      • 5.19 Cohens Kappa Measure
        01:22
      • 5.20 Recap
        00:42

    • Lesson 06: Unsupervised Algorithms

      • 6.01 Introduction
        00:53
      • 6.02 What are Unsupervised Algorithms?
        02:51
      • 6.03 Types of Unsupervised Algorithms Clustering and Associative
        02:15
      • 6.04 When to Use Unsupervised Algorithms?
        01:22
      • 6.05 Visualizing Outputs
        06:14
      • 6.06 Performance Parameters
        02:55
      • 6.07 Clustering Types
        00:56
      • 6.08 Hierarchical Clustering
        03:32
      • 6.09 Applying Hierarchical Clustering
        03:22
      • 6.10 K means Clustering: Part 1
        02:30
      • 6.11 K means Clustering: Part 2
        01:54
      • 6.12 Applying K Means Clustering
        03:37
      • 6.13 KNN-K Nearest Neighbors
        03:41
      • 6.14 Outlier Detection
        01:47
      • 6.15 Outlier Detection Algorithms in PyOD
        02:49
      • 6.16 Demo: K NN for Anomaly Detection
        02:37
      • 6.17 Principal Component Analysis
        04:15
      • 6.18 Applying Principal Component Analysis: PCA
        04:21
      • 6.19 Correspondence Analysis Multiple correspondence analysis: MCA
        03:16
      • 6.20 Singular Value Decomposition
        02:06
      • 6.21 Applying Singular Value Decomposition
        04:14
      • 6.22 Independent Component Analysis
        02:26
      • 6.23 Applying Independent Component Analysis
        01:54
      • 6.24 BIRCH
        02:33
      • 6.25 Applying BIRCH
        02:15
      • 6.26 Recap
        01:05
      • 6.27 Spotlight
        03:20

    • Lesson 07: Ensemble Learning

      • 7.01 Introduction
        00:54
      • 7.02 What is Ensemble Learning?
        01:46
      • 7.03 Categories in Ensemble Learning
        02:47
      • 7.04 Sequential Ensemble Technique
        02:50
      • 7.05 Parallel Ensemble Technique
        02:10
      • 7.06 Types of Ensemble Methods
        01:56
      • 7.07 Bagging
        03:01
      • 7.08 Demo: Bagging
        02:53
      • 7.09 Boosting
        02:14
      • 7.10 Demo: Boosting
        03:29
      • 7.11 Stacking
        02:56
      • 7.12 Demo: Stacking
        03:44
      • 7.13 Reducing Errors with Ensembles
        05:27
      • 7.14 Applying Averaging and Max Voting
        06:33
      • 7.15 Hello World Tensorflow
        02:38
      • 7.16 Hands on with TensorFlow: Part A
        05:09
      • 7.17 Keras
        02:49
      • 7.18 Hands on with TensorFlow: Part B
        05:57
      • 7.19 Recap
        00:45

    • Lesson 08: Recommender System

      • 8.01 Introduction
        01:00
      • 8.02 How do recommendation engines work
        02:45
      • 8.03 Recommendation Engine: Use Cases
        01:44
      • 8.04 Examples of Recommender System and Their Designs
        02:55
      • 8.05 Leveraging PyTorch to Build a Recommendation Engine
        02:23
      • 8.06 Collaborative Filtering and Memory Based Modeling
        06:31
      • 8.07 Item Based Collaborative Filtering
        07:02
      • 8.08 User Based Collaborative Filtering
        13:05
      • 8.09 Model Based Collaborative Filtering
        04:09
      • 8.10 Dimensionality Reduction and Matrix Factorization
        04:51
      • 8.11 Accuracy Matrices in ML
        08:06
      • 8.12 Recap
        00:52
      • 8.13 Spotlight
        03:01

Career Opportunities

About the Course

Machine learning is becoming an important skill across industries, and this free course helps you build a strong foundation. Learn how to use Python to create models, identify patterns, and work with real-world data. The course combines concepts with practical projects, helping you understand different methods and when to apply them. Whether you are starting your ML journey or improving your basics, this course covers fundamentals, ensemble methods, and deep learning in a beginner-friendly way.


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Why you should learn Machine Learning?

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AI and big data lead the fastest-growing skills to 2030.

FAQs

  • What is Machine Learning?

    Machine learning is a branch of artificial intelligence where systems learn from data to make predictions, find patterns, and improve their performance over time without being explicitly programmed for each task. This free machine learning course covers the core algorithms, techniques, and practical applications that define how ML is used across industries today.

  • Who should enroll in this Free Machine Learning Course?

    This free machine learning course online is designed for students and fresh graduates exploring data science careers, software developers looking to add ML to their skill set, business professionals curious about AI and data-driven decision-making, and anyone who wants a structured, beginner-friendly introduction to machine learning without a financial barrier.

  • Is this Free Machine Learning Course completely free?

    Yes, this free machine learning course with certificate is completely free. You get full access to every lesson and receive a certificate upon completion at no cost, with no hidden fees at any stage of this free ML course.

  • Does this Free Machine Learning Course include a certificate?

    Yes, this free ML course with certificate includes a free certificate of completion you can add to your LinkedIn profile or resume to demonstrate your machine learning knowledge and commitment to professional development to potential employers.

  • How can I enroll in this Free Machine Learning Course Online?

    Enrolling is straightforward: visit the SkillUP platform, find this free machine learning course online, and sign up for free. No payment details are required and you can start learning immediately at your own pace without any fixed schedule or deadlines.

  • What topics are covered in this Free ML Course?

    This free ML course covers: 

    • Introduction to Machine Learning

    • Supervised and Unsupervised Learning

    • Regression and Its Applications

    • Classification Algorithms (Logistic Regression, Kernel SVM, Naive Bayes)

    • Clustering with K-Means

    • Ensemble Learning (Decision Trees, Random Forests, Boosting, Bagging)

    • Recommender Systems

    • Deep Learning Fundamentals

  • What skills will I gain from this Free Machine Learning Course?

    Completing this free machine learning course will give you practical skills in 

    • Supervised and unsupervised learning

    • Linear and logistic regression

    • Classification and clustering

    • Decision trees and random forests

    • Ensemble methods

    • Support vector machines

    • Recommender systems

    • Deep learning fundamentals and

    • Machine learning with Python.

  • Are there any prerequisites for joining this Free Machine Learning Course?

    No formal prerequisites are required for this free machine learning course online. A basic familiarity with Python programming will help you follow along with the practical sections more smoothly, though the course is designed to be accessible to motivated beginners without an extensive technical background.

  • What is the difference between Generative AI vs Machine Learning?

    • Machine learning is a broad field focused on building systems that learn from data to make predictions and decisions.

    • Generative AI is a specific application of machine learning focused on creating new content like text, images, music, and more, using models like large language models and GANs. This free ML course covers the foundational machine learning concepts that underpin generative AI and other advanced AI applications.

  • Which industries hire Machine Learning professionals?

    • Technology: Use machine learning to develop software solutions, automate processes, and improve applications.

    • Finance: Apply ML models for fraud detection, risk assessment, and financial analysis.

    • Healthcare: Use machine learning for research, diagnostics, and analyzing patient data.

    • Retail & Marketing: Improve customer insights, recommendations, and personalized experiences.

    • Manufacturing & Logistics: Optimize operations, forecasting, supply chains, and maintenance processes.

    • Education & Other Industries: Apply ML skills across sectors that rely on data-driven decision-making.

  • Why do learners choose Simplilearn for online courses?

    Simplilearn is a globally trusted learning platform used by millions of professionals and students worldwide. SkillUP by Simplilearn offers high-quality free machine learning courses with certificates alongside programs in AI, data analytics, cloud, and more, all at no cost. Simplilearn holds a TrustScore of 4.5 out of 5 on Trustpilot based on over 31,795 reviews, with an average rating of 4.6 out of 5 across Trustpilot, SwitchUp, and Course Report.

  • What job roles can I apply for after completing this Free ML Course?

    • Machine Learning Engineer: Build and deploy ML models using algorithms, data, and programming skills.

    • Data Scientist: Analyze complex datasets and create predictive models to solve business problems.

    • AI Engineer: Develop intelligent applications using machine learning and AI technologies.

    • Junior Data Analyst: Use data analysis techniques to identify insights and support decision-making.

    • Business Analyst with ML Focus:Apply machine learning insights to improve business strategies and processes.

    • ML Consultant: Help organizations implement machine learning solutions for real-world challenges.

  • What is the Machine Learning roadmap for beginners after completing this course?

    • Build an ML Portfolio: Create practical machine learning projects and showcase your work on GitHub.

    • Learn Advanced Frameworks: Explore deep learning tools like TensorFlow and PyTorch to strengthen your ML skills.

    • Gain Real-World Experience: Participate in Kaggle competitions and ML projects to improve problem-solving abilities.

    • Pursue Advanced Certifications: Earn certifications in data science, AI, or machine learning to enhance your profile.

    • Specialize in AI Domains: Focus on high-demand areas like Natural Language Processing (NLP) or Computer Vision.

    • Start Your ML Career: Apply for entry-level ML and data science roles with companies developing AI-powered solutions.

Learner Review

  • M Ehsani

    M Ehsani

    Thanks to Simplilearn for providing such an insightful course on Machine Learning. Looking forward to applying my learnings in a real-world project.

  • Jyoti Dange

    Jyoti Dange

    The course was really good. I am thorough with the fundamentals of Machine Learning and I have recommended this course to my friends.

  • Rajeev Gaur

    Rajeev Gaur

    The course gave me a lot of exposure to the practical side of Machine Learning projects. It was an awesome experience.

  • Daren Lee

    Daren Lee

    The course material covered concepts with clarity through real-life examples.

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